{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.random.uniform(0,100,100)\n",
    "y=1.5 * x ** 2 + 2 * x + 1 + np.random.normal(len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x112f44e10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11301aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=x.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=lr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11092d5f8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11092d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_predict,color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_h = np.hstack([X,(x**2).reshape(-1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr1=LinearRegression()\n",
    "lr1.fit(X_h,y)\n",
    "y_predict1 = lr1.predict(X_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112c9f198>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a113c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_predict,color='g')\n",
    "plt.plot(np.sort(x),y_predict1[np.argsort(x)],color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
